292 lines
11 KiB
Python
292 lines
11 KiB
Python
import os
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import time
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import logging
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import subprocess
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from typing import Dict, Tuple
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import pandas as pd
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import numpy as np
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from osgeo import gdal
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class NotOverlapError(Exception):
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"""图像重叠度不足异常"""
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pass
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class ODMProcessMonitor:
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"""ODM处理监控器"""
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def __init__(self, output_dir: str, mode: str = "快拼模式"):
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self.output_dir = output_dir
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self.logger = logging.getLogger('UAV_Preprocess.ODMMonitor')
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self.mode = mode
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def _check_success(self, grid_dir: str) -> bool:
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"""检查ODM是否执行成功
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检查项目:
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1. 必要的文件夹和文件是否存在
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2. 产品文件是否有效
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"""
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project_dir = os.path.join(grid_dir, 'project')
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# 根据不同模式检查不同的产品
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if self.mode == "快拼模式":
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# 只检查正射影像
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# if not self._check_orthophoto(project_dir):
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# return False
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pass
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elif self.mode == "三维模式":
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# 检查点云和实景三维
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if not all([
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os.path.exists(os.path.join(project_dir, 'odm_georeferencing', 'odm_georeferenced_model.laz')),
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os.path.exists(os.path.join(project_dir, 'odm_texturing', 'odm_textured_model_geo.obj'))
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]):
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self.logger.error("点云或实景三维文件夹未生成")
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return False
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# TODO: 添加点云和实景三维的质量检查
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elif self.mode == "重建模式":
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# 检查所有产品
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if not all([
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os.path.exists(os.path.join(project_dir, 'odm_georeferencing', 'odm_georeferenced_model.laz')),
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os.path.exists(os.path.join(project_dir, 'odm_texturing', 'odm_textured_model_geo.obj'))
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]):
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self.logger.error("部分必要的文件夹未生成")
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return False
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# 检查正射影像
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# if not self._check_orthophoto(project_dir):
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# return False
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# TODO: 添加点云和实景三维的质量检查
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return True
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# TODO 正射影像怎么检查最好
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def _check_orthophoto(self, project_dir: str) -> bool:
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"""检查正射影像的质量"""
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ortho_path = os.path.join(project_dir, 'odm_orthophoto', 'odm_orthophoto.original.tif')
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if not os.path.exists(ortho_path):
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self.logger.error("正射影像文件未生成")
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return False
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# 检查文件大小
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file_size_mb = os.path.getsize(ortho_path) / (1024 * 1024) # 转换为MB
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if file_size_mb < 1:
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self.logger.error(f"正射影像文件过小: {file_size_mb:.2f}MB")
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return False
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try:
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# 打开影像文件
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ds = gdal.Open(ortho_path)
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if ds is None:
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self.logger.error("无法打开正射影像文件")
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return False
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# 读取第一个波段
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band = ds.GetRasterBand(1)
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# 获取统计信息
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stats = band.GetStatistics(False, True)
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if stats is None:
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self.logger.error("无法获取影像统计信息")
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return False
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min_val, max_val, mean, std = stats
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# 计算空值比例
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no_data_value = band.GetNoDataValue()
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array = band.ReadAsArray()
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if no_data_value is not None:
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no_data_ratio = np.sum(array == no_data_value) / array.size
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else:
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no_data_ratio = 0
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# 检查空值比例是否过高（超过50%）
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if no_data_ratio > 0.5:
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self.logger.error(f"正射影像空值比例过高: {no_data_ratio:.2%}")
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return False
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# 检查影像是否全黑或全白
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if max_val - min_val < 1:
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self.logger.error("正射影像可能无效：像素值范围过小")
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return False
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ds = None # 关闭数据集
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return True
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except Exception as e:
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self.logger.error(f"检查正射影像时发生错误: {str(e)}")
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return False
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def run_odm_with_monitor(self, grid_dir: str, grid_id: tuple, produce_dem: bool = False) -> Tuple[bool, str]:
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"""运行ODM命令"""
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# if produce_dem and self.mode == "快拼模式":
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# self.logger.error("快拼模式下无法生成DEM，请调整生产参数")
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# return False, "快拼模式下无法生成DEM，请调整生产参数"
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self.logger.info(f"开始处理网格 ({grid_id[0]},{grid_id[1]})")
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success = False
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error_msg = ""
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max_retries = 3
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current_try = 0
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use_lowest_quality = True # 初始使用lowest quality
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while current_try < max_retries:
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current_try += 1
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self.logger.info(
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f"第 {current_try} 次尝试处理网格 ({grid_id[0]},{grid_id[1]})")
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try:
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# 构建Docker命令
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grid_dir = grid_dir[0].lower()+grid_dir[1:].replace('\\', '/')
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docker_command = (
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f"docker run --gpus all -ti --rm "
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f"-v {grid_dir}:/datasets "
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f"opendronemap/odm:gpu "
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f"--project-path /datasets project "
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f"--max-concurrency 15 "
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f"--force-gps "
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)
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# 根据是否使用lowest quality添加参数
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if use_lowest_quality:
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docker_command += f"--feature-quality lowest "
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# docker_command += f"--orthophoto-resolution 8 "
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if produce_dem:
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docker_command += (
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f"--dsm "
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f"--dtm "
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)
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if self.mode == "快拼模式":
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docker_command += (
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#f"--fast-orthophoto "
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f"--skip-3dmodel "
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)
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elif self.mode == "三维模式":
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docker_command += (
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f"--skip-orthophoto "
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)
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docker_command += "--rerun-all"
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self.logger.info(docker_command)
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result = subprocess.run(
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docker_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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stdout, stderr = result.stdout.decode(
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'utf-8'), result.stderr.decode('utf-8')
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stdout_lines = stdout.strip().split('\n')
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last_lines = '\n'.join(
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stdout_lines[-50:] if len(stdout_lines) > 10 else stdout_lines)
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self.logger.info(f"==========stdout==========: {last_lines}")
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if stderr:
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self.logger.error(f"docker run指令执行失败")
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self.logger.error(f"==========stderr==========: {stderr}")
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if "error during connect" in stderr or "The system cannot find the file specified" in stderr:
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error_msg = "Docker没有启动，请启动Docker"
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elif "user declined directory sharing" in stderr:
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error_msg = "Docker无法访问目录，请检查目录权限和共享设置"
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else:
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error_msg = "Docker运行失败，需要人工排查错误"
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break
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else:
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self.logger.info("docker run指令执行成功")
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if "ODM app finished" in last_lines:
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self.logger.info("ODM处理完成")
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if self._check_success(grid_dir):
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self.logger.info(
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f"网格 ({grid_id[0]},{grid_id[1]}) 处理成功")
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success = True
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error_msg = ""
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break
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else:
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self.logger.error(
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f"虽然ODM处理完成，但是生产产品质量可能不合格，需要人工检查")
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raise NotOverlapError
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# TODO 先写成这样，后面这三种情况可能处理不一样
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elif "enough overlap" in last_lines:
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raise NotOverlapError
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elif "out of memory" in last_lines:
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raise NotOverlapError
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elif "strange values" in last_lines:
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raise NotOverlapError
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else:
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raise NotOverlapError
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except NotOverlapError:
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if use_lowest_quality:
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self.logger.warning(
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"检测到not overlap错误，移除lowest quality参数后重试")
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use_lowest_quality = False
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time.sleep(10)
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continue
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else:
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self.logger.error(
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"即使移除lowest quality参数后仍然出现错误")
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error_msg = "图像重叠度不足，需要人工检查数据集的采样间隔情况"
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break
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return success, error_msg
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def process_all_grids(self, grid_points: Dict[tuple, pd.DataFrame], produce_dem: bool) -> Dict[tuple, pd.DataFrame]:
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"""处理所有网格
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Returns:
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Dict[tuple, pd.DataFrame]: 成功处理的网格点数据字典
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"""
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self.logger.info("开始执行网格处理")
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successful_grid_points = {}
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failed_grids = []
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for grid_id, points in grid_points.items():
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grid_dir = os.path.join(
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self.output_dir, f'grid_{grid_id[0]}_{grid_id[1]}'
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)
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try:
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success, error_msg = self.run_odm_with_monitor(
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grid_dir=grid_dir,
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grid_id=grid_id,
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produce_dem=produce_dem
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)
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if success:
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successful_grid_points[grid_id] = points
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else:
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self.logger.error(
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f"网格 ({grid_id[0]},{grid_id[1]}) 处理失败: {error_msg}")
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failed_grids.append((grid_id, error_msg))
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except Exception as e:
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error_msg = str(e)
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self.logger.error(
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f"处理网格 ({grid_id[0]},{grid_id[1]}) 时发生异常: {error_msg}")
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failed_grids.append((grid_id, error_msg))
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# 汇总处理结果
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total_grids = len(grid_points)
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failed_count = len(failed_grids)
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success_count = len(successful_grid_points)
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self.logger.info(
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f"网格处理完成。总计: {total_grids}, 成功: {success_count}, 失败: {failed_count}")
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if failed_grids:
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self.logger.error("失败的网格:")
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for grid_id, error_msg in failed_grids:
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self.logger.error(
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f"网格 ({grid_id[0]},{grid_id[1]}): {error_msg}")
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if len(successful_grid_points) == 0:
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raise Exception("所有网格处理都失败，无法继续处理")
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return successful_grid_points
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